Success in business depends on multiple factors. In the case of retail businesses, one of the most important factors is location. An optimal location will have favorable traffic flow, adequate parking, competitive cost, an inviting appearance, as well as suitable customer demographics and spending patterns. Location of competitors and the location of anchor businesses (i.e. nearby businesses that attract customers that would be also suitable for one's own business) are additional considerations. Barriers, including barriers to traffic flow and zoning restrictions, are further considerations. There are many more considerations, depending on the nature of the business.
Success is in business may be measured by earnings, profitability, increases in total value, increases in membership (particular for non-profit businesses), and sometimes increased exposure in a target market is a legitimate measure of business success.
Currently, some companies primarily rely upon human insight and experience to predict business performance for proposed business locations. Others recognize that there is added value in analyzing the multitude of data available that could be used to support a decision-maker. Unfortunately, available data is often too difficult for an individual to readily assimilate and fully comprehend. In some instances, complex data relationships that can optimize business location selection are undetectable when raw and non-homogeneous data is viewed with human eyes. In other instances, statistical methods may fail to yield information that directly relates to predicting business success.
It is desirable for complex relationships to be extracted from non-homogeneous data and used to assist a decision-maker in choosing an optimal business location and for making predictions about business locations. It is also desirable that such complex relationships can be presented in view of particular business goals.